## Step 1: Define the symbolic representation of the problem

Let's denote the units of medication A and B as $x_1$ and $x_2$ respectively. The problem can be represented symbolically as follows:

- **Variables:** 
  - $x_1$ : units of medication A
  - $x_2$ : units of medication B

- **Objective Function:** The cost per unit of medication A is $1, and the cost per unit of medication B is $2. The objective is to minimize the total cost: $1x_1 + 2x_2$.

- **Constraints:**
  - Cough relief: $1x_1 + 3x_2 \geq 20$
  - Pain relief: $2x_1 + 1x_2 \geq 25$
  - Fever relief: $3x_1 + 2x_2 \geq 30$
  - Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## 2: Convert the symbolic representation into a Gurobi code

We will use the Gurobi library in Python to solve this linear programming problem.

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(lb=0, name="medication_A")
x2 = model.addVar(lb=0, name="medication_B")

# Define the objective function
model.setObjective(x1 + 2*x2, gurobi.GRB.MINIMIZE)

# Define the constraints
model.addConstr(x1 + 3*x2 >= 20, name="cough_relief")
model.addConstr(2*x1 + x2 >= 25, name="pain_relief")
model.addConstr(3*x1 + 2*x2 >= 30, name="fever_relief")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Medication A: {x1.varValue}")
    print(f"Medication B: {x2.varValue}")
    print(f"Total Cost: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 3: Provide the symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'units of medication A'), ('x2', 'units of medication B')],
    'objective_function': '1*x1 + 2*x2',
    'constraints': [
        '1*x1 + 3*x2 >= 20',
        '2*x1 + 1*x2 >= 25',
        '3*x1 + 2*x2 >= 30',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

The final answer is: 
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(lb=0, name="medication_A")
x2 = model.addVar(lb=0, name="medication_B")

# Define the objective function
model.setObjective(x1 + 2*x2, gurobi.GRB.MINIMIZE)

# Define the constraints
model.addConstr(x1 + 3*x2 >= 20, name="cough_relief")
model.addConstr(2*x1 + x2 >= 25, name="pain_relief")
model.addConstr(3*x1 + 2*x2 >= 30, name="fever_relief")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Medication A: {x1.varValue}")
    print(f"Medication B: {x2.varValue}")
    print(f"Total Cost: {model.objVal}")
else:
    print("No optimal solution found.")
```